Analysis of Panel Data by Cheng Hsiao PDF

By Cheng Hsiao

ISBN-10: 0511061404

ISBN-13: 9780511061400

ISBN-10: 0521522714

ISBN-13: 9780521522717

ISBN-10: 0521818559

ISBN-13: 9780521818551

This e-book offers a accomplished, coherent, and intuitive evaluate of panel information methodologies which are beneficial for empirical research. considerably revised from the second one variation, it comprises new chapters on modeling cross-sectionally based information and dynamic structures of equations. a number of the extra advanced innovations were extra streamlined. different new fabric comprises correlated random coefficient types, pseudo-panels, length and count number facts types, quantile research, and replacement methods for controlling the impression of unobserved heterogeneity in nonlinear panel information types.

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The between-group and within-group variations are just added up. Thus, one can view the OLS and LSDV as somewhat all-or-nothing ways of utilizing the between-group variation. 12). If [Wx˜ x˜ + ψ Bx˜ x˜ ] is nonsingular, the covariance matrix of GLS estimators of ␦ can be written as Var µˆ ␤ˆ = σu2 [Wx˜ x˜ + ψ Bx˜ x˜ ]−1 GLS   = σu2  0  0 N 0 Xi Q Xi   i=1  −1 N  N  +Tψ   N  x¯ i      x¯ i x¯ i i=1 N x¯ i i=1 . , Rao (1973, Chapter 2); Theil (1971, Chapter 1)), we obtain N Var(␤ˆ GLS ) = σu2 Xi Q Xi + T ψ i=1 −1 N (¯xi − x¯ )(¯xi − x¯ ) .

N , t = 1, . . 1) where αit∗ and ␤it = (β1it , β2it , . . 2 Analysis of Covariance 15 constants that vary across i and t, respectively, xit = (x1it , . . , x K it ) is a 1 × K vector of exogenous variables, and u it is the error term. Two aspects of the estimated regression coefficients can be tested: first, the homogeneity of regression slope coefficients; second, the homogeneity of regression intercept coefficients. The test procedure has three main steps: 1. Test whether or not slopes and intercepts simultaneously are homogeneous among different individuals at different times.

Utilizing the restriction solving the marginal conditions, we have µˆ = y¯ − ␤ x¯ , where y¯ = 1 NT 1 x¯ = NT αˆ i = y¯ i − µˆ − ␤ x¯ i . 5). 2, we discussed the estimation of linear-regression models when the effects of omitted individual-specific variables (αi ) are treated as fixed constants over time. In this section we treat the individual-specific effects, like u it , as random variables. It is a standard practice in the regression analysis to assume that the large number of factors that affect the value of the dependent variable, but that have not been explicitly included as independent variables, can be appropriately summarized by a random disturbance.

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Analysis of Panel Data by Cheng Hsiao


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